Identification of potentially cancerous lesions in computed tomography (CT) and magnetic resonance imaging (MRI) studies is a common task of radiologists. An aspect of this task relates to measurement of lesion volume and density, both important in assessing the temporal response of a tumor to treatment.
RECIST (Response Evaluation Criteria In Solid Tumors) is a set of published rules that define when cancer patients improve (“respond”), stay the same (“stable”) or worsen (“progression”) during treatments. Typically, a uni-dimensional measurement, RECIST criteria, which relates to the longest diameter of the tumor, is used as a type of surrogate for 3D volume measurement. These linear measurements, obtained from manual radiologist mark-up, are of some value but are generally not sufficient for accurately tracking the volume change of irregularly shaped tumors. A more accurate volumetric measurement requires the radiologist to manually segment the tumor. This process is a tedious, time-consuming task and is often poorly suited to the overall workflow requirements of the radiologist. In addition, the results of manual segmentation can be highly dependent upon the observer and are often not reproducible. Therefore, there is a need to automate the manual tasks performed by the radiologist in order to improve the workflow and to resolve inter- and intra-observer variability.
Unenhanced CT images are generally insufficient for detection of lesions because there is very little difference in attenuation values between the lesion and parenchyma, or surrounding tissue, in the liver. Lesion-to-liver contrast can be greatly improved by the use of contrast agents. Routinely, multiphase (precontrast, arterial, portal, and delayed), contrast-enhanced series are performed to facilitate the detection and characterization of liver lesions.
Once detected, segmentation of liver lesions is a challenging task because of the large variability of intensity values, in Hounsfield units (HU), between the lesion and parenchyma in the liver. The perceived density of the lesion and parenchyma are dependent upon factors such as the tumor type, contrast agent, contrast timing, and patient physiology. Compared to the surrounding parenchyma, lesions can be either hyperdense (brighter, at a higher intensity value) or hypodense (darker, at a lower intensity value).
A number of methods have been proposed for automating the process of segmenting liver lesions in CT images. A number of proposed techniques utilize a combination of a classification step, utilizing a priori information, followed by a deformable surface evolution technique, such as the active contour model. Constrained, seeded, region-growing techniques are also another common method used for segmenting liver lesions. Typically, features are generated from a number of seed points, and the initial region is iteratively grown by incorporating neighboring voxels that have similar features. The resulting tumor segmentation is often refined further by applying a postprocessing step to the binary segmentation results via morphological operations.
While existing methods for liver lesion segmentation have achieved a measure of success, there remains considerable room for improvement. One persistent problem, due to the properties of the parenchyma and surrounding tissues within and outside of the liver, relates to leakage, a segmentation phenomenon in which the boundaries of an identified lesion may not be clearly distinguishable from nearby tissues, causing the segmentation algorithm to mistakenly categorize non-lesion tissue as belonging to the lesion.
Thus, it is seen that there is a need for segmentation techniques that allow more accurate identification of the lesion boundaries in 3-D volume images.